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The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

机译：基于LiDAR的DSM评估低成本无人机的基于摄影测量的DSM

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Background and Purpose: Unmanned aerial vehicles (UAVs) are flexible to solve various surveying tasks which make them useful in many disciplines, including forestry. The main goal of this research is to evaluate the quality of photogrammetry-based digital surface model (DSM) from low-cost UAV’s images collected in non-optimal weather (windy and cloudy weather) and environmental (inaccessibility for regular spatial distribution of ground control points - GCPs) conditions. Materials and Methods: The UAV-based DSMs without (DSMP) and with using GCPs (DSMP-GCP) were generated. The vertical agreement assessment of the UAV-based DSMs was conducted by comparing elevations of 60 checkpoints of a regular 100 m sampling grid obtained from LiDAR-based DSM (DSML) with the elevations of planimetrically corresponding points obtained from DSMP and DSMP-GCP. Due to the non-normal distribution of residuals (vertical differences between UAV- and LiDAR-based DSMs), a vertical agreement was assessed by using robust measures: median, normalised median absolute deviation (NMAD), 68.3% quantile and 95% quantile. Results: As expected, DSMP-GCP shows higher accuracy, i.e. higher vertical agreement with DSML than DSMP. The median, NMAD, 68.3% quantile, 95% quantile and RMSE* (without outliers) values for DSMP are 2.23 m, 3.22 m, 4.34 m, 15.04 m and 5.10 m, respectively, whereas for DSMP-GCP amount to -1.33 m, 2.77 m, 0.11 m, 8.15 m and 3.54 m, respectively. Conclusions: The obtained results confirmed great potential of images obtained by low-cost UAV for forestry applications, even if they are surveyed in non-optimal weather and environmental conditions. This could be of importance for cases when urgent UAV surveys are needed (e.g. detection and estimation of forest damage) which do not allow careful and longer survey planning. The vertical agreement assessment of UAV-based DSMs with LiDAR-based DSM confirmed the importance of GCPs for image orientation and DSM generation. Namely, a considerable improvement in vertical accuracy of UAV-based DSMs was observed when GCPs were used.
机译：背景与目的：无人机可以灵活地解决各种测量任务，从而使其在包括林业在内的许多学科中都非常有用。这项研究的主要目的是从在非最佳天气（刮风和多云天气）和环境（无法获得地面控制的规则空间分布）中收集的低成本无人机图像评估基于摄影测量的数字表面模型（DSM）的质量点-GCP）条件。材料和方法：生成了不带（DSMP）和带GCP（DSMP-GCP）的基于无人机的DSM。通过比较从基于LiDAR的DSM（DSML）获得的常规100 m采样网格的60个检查点的高程与从DSMP和DSMP-GCP获得的平面对应点的高程，对基于无人机的DSM进行垂直一致性评估。由于残差的非正态分布（基于无人机和基于LiDAR的DSM之间存在垂直差异），因此通过使用稳健的指标评估了垂直协议：中位数，标准化中位数绝对偏差（NMAD），68.3％的分位数和95％的分位数。结果：正如预期的那样，DSMP-GCP显示出更高的准确性，即与DSML的垂直一致性高于DSMP。 DSMP的中值，NMAD，68.3％的分位数，95％的分位数和RMSE *（无异常值）值分别为2.23 m，3.22 m，4.34 m，15.04 m和5.10 m，而DSMP-GCP的中值为-1.33 m分别为2.77 m，0.11 m，8.15 m和3.54 m。结论：即使在非最佳天气和环境条件下进行调查，所获得的结果也证实了低成本无人机在林业应用中获得的巨大潜力。这对于需要紧急UAV调查（例如，对森林破坏的检测和估计）而又无法进行仔细且更长的调查计划的情况可能非常重要。基于无人机的DSM与基于LiDAR的DSM的垂直协议评估证实了GCP对于图像方向和DSM生成的重要性。即，当使用GCP时，观察到了基于UAV的DSM的垂直精度的显着提高。

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